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All Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika International Journal of Applied Mathematics, Sciences, and Technology for National Defense
Sarah Fadhlia
Universitas Indraprasta PGRI Jakarta
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Physics

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ANALISIS GEROMBOL PADA DATA DERET WAKTU PENDERITA COVID-19 PROVINSI JAWA BARAT Sarah Fadhlia
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 2 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i2.361

Abstract

This study aims to apply clustering techniques to time series data. Time series models can be formed for all research data objects, so many research objects need to be grouped so that the resulting model becomes more efficient. The object used in this study was data on Covid-19 sufferers from 27 regencies and cities in West Java Province. All objects were analyzed by time series to produce 27 models. All objects' data patterns and models have many similarities, so clustering can be done. Clustering models using the Ward method and the Piccolo dissimilarity measure. The optimum cluster uses the Hartigan and Ball indices to obtain 3 clusters
Times series data analysis: The Holt-Winters model for rainfall prediction In West Java Eko Primadi Hendri; Sarah Fadhlia
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 2 No. 1 (2024): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v2i1.325

Abstract

Time series data analysis is used to analyze data that considers time and data characteristics to predict future events. One of the time series data is rainfall data. Rainfall data has a seasonal pattern because there is a pattern that repeats itself over a certain period. Data analysis that considers the characteristics of seasonal patterns is the Holt-Winters method. The Holt-Winters model is divided into two, namely additive and multiplicative models. This research aims to compare the Holt-Winters additive and multiplicative methods to see the accuracy in predicting rainfall data in West Java. The additive model has level parameter I±=0,435, trend parameter I²=0, seasonal parameter I³=1, and RMSE value 140,174. The multiplicative model has level parameter I±=0,936, trend parameter I²=0, seasonal parameter I³=0,247, and RMSE value 150,020. The additive model has a smaller RMSE value so it can predict future rainfall with greater accuracy.
Co-Authors Eko Primadi Hendri